Overview

Dataset statistics

Number of variables17
Number of observations333
Missing cells108
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.8 KiB
Average record size in memory122.4 B

Variable types

Numeric14
Boolean2
Text1

Alerts

commercial_property is highly overall correlated with crime_rate and 7 other fieldsHigh correlation
county is highly overall correlated with household_affluency and 2 other fieldsHigh correlation
crime_rate is highly overall correlated with commercial_property and 7 other fieldsHigh correlation
household_affluency is highly overall correlated with commercial_property and 8 other fieldsHigh correlation
household_size is highly overall correlated with county and 2 other fieldsHigh correlation
normalised_sales is highly overall correlated with commercial_property and 8 other fieldsHigh correlation
property_value is highly overall correlated with commercial_property and 6 other fieldsHigh correlation
proportion_flats is highly overall correlated with commercial_property and 4 other fieldsHigh correlation
proportion_newbuilds is highly overall correlated with commercial_property and 7 other fieldsHigh correlation
proportion_nonretail is highly overall correlated with commercial_property and 7 other fieldsHigh correlation
public_transport_dist is highly overall correlated with commercial_property and 6 other fieldsHigh correlation
school_proximity is highly overall correlated with normalised_salesHigh correlation
new_store is highly imbalanced (67.2%)Imbalance
is_train is highly imbalanced (76.2%)Imbalance
commercial_property has 30 (9.0%) missing valuesMissing
school_proximity has 65 (19.5%) missing valuesMissing
normalised_sales has 13 (3.9%) missing valuesMissing
location_id has unique valuesUnique
proportion_flats has 248 (74.5%) zerosZeros
proportion_newbuilds has 26 (7.8%) zerosZeros

Reproduction

Analysis started2024-02-09 18:08:07.091888
Analysis finished2024-02-09 18:08:23.573911
Duration16.48 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

location_id
Real number (ℝ)

UNIQUE 

Distinct333
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.73273
Minimum1
Maximum506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:23.649834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.6
Q1127
median250
Q3376
95-th percentile474.4
Maximum506
Range505
Interquartile range (IQR)249

Descriptive statistics

Standard deviation145.11318
Coefficient of variation (CV)0.57645733
Kurtosis-1.1885492
Mean251.73273
Median Absolute Deviation (MAD)125
Skewness-0.0122536
Sum83827
Variance21057.835
MonotonicityNot monotonic
2024-02-09T18:08:23.798510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
464 1
 
0.3%
87 1
 
0.3%
169 1
 
0.3%
241 1
 
0.3%
469 1
 
0.3%
283 1
 
0.3%
7 1
 
0.3%
313 1
 
0.3%
190 1
 
0.3%
51 1
 
0.3%
Other values (323) 323
97.0%
ValueCountFrequency (%)
1 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
7 1
0.3%
10 1
0.3%
11 1
0.3%
12 1
0.3%
13 1
0.3%
15 1
0.3%
ValueCountFrequency (%)
506 1
0.3%
504 1
0.3%
503 1
0.3%
501 1
0.3%
500 1
0.3%
498 1
0.3%
494 1
0.3%
491 1
0.3%
489 1
0.3%
488 1
0.3%

crime_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct332
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7971859
Minimum0.0071416
Maximum83.093533
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:23.930603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0071416
5-th percentile0.02682168
Q10.0892248
median0.2957097
Q34.1563886
95-th percentile17.200318
Maximum83.093533
Range83.086391
Interquartile range (IQR)4.0671638

Descriptive statistics

Standard deviation8.3080672
Coefficient of variation (CV)2.1879538
Kurtosis30.924065
Mean3.7971859
Median Absolute Deviation (MAD)0.261256
Skewness4.5989812
Sum1264.4629
Variance69.02398
MonotonicityNot monotonic
2024-02-09T18:08:24.050980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0169613 2
 
0.6%
17.600541 1
 
0.3%
7.6853221 1
 
0.3%
0.1857607 1
 
0.3%
0.142945 1
 
0.3%
10.5418717 1
 
0.3%
1.8403632 1
 
0.3%
0.2328704 1
 
0.3%
3.574868 1
 
0.3%
0.0222045 1
 
0.3%
Other values (322) 322
96.7%
ValueCountFrequency (%)
0.0071416 1
0.3%
0.0102378 1
0.3%
0.0123848 1
0.3%
0.0147013 1
0.3%
0.0148143 1
0.3%
0.015368 1
0.3%
0.0161816 1
0.3%
0.0162607 1
0.3%
0.0169613 2
0.6%
0.0200914 1
0.3%
ValueCountFrequency (%)
83.093533 1
0.3%
51.693093 1
0.3%
43.337534 1
0.3%
42.557947 1
0.3%
32.381054 1
0.3%
29.312878 1
0.3%
28.302093 1
0.3%
28.025921 1
0.3%
27.564994 1
0.3%
25.534723 1
0.3%

proportion_flats
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.689189
Minimum0
Maximum100
Zeros248
Zeros (%)74.5%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:24.161691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile77
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation22.674762
Coefficient of variation (CV)2.1212799
Kurtosis4.8793739
Mean10.689189
Median Absolute Deviation (MAD)0
Skewness2.3740517
Sum3559.5
Variance514.14482
MonotonicityNot monotonic
2024-02-09T18:08:24.268775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 248
74.5%
20 14
 
4.2%
80 7
 
2.1%
22 7
 
2.1%
25 7
 
2.1%
12.5 6
 
1.8%
45 5
 
1.5%
33 3
 
0.9%
55 3
 
0.9%
90 3
 
0.9%
Other values (15) 30
 
9.0%
ValueCountFrequency (%)
0 248
74.5%
12.5 6
 
1.8%
17.5 1
 
0.3%
18 1
 
0.3%
20 14
 
4.2%
21 3
 
0.9%
22 7
 
2.1%
25 7
 
2.1%
28 2
 
0.6%
30 3
 
0.9%
ValueCountFrequency (%)
100 1
 
0.3%
95 3
0.9%
90 3
0.9%
85 2
 
0.6%
82.5 1
 
0.3%
80 7
2.1%
75 3
0.9%
60 3
0.9%
55 3
0.9%
52.5 1
 
0.3%

proportion_nonretail
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.293483
Minimum0.74
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:24.395572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile2.18
Q15.13
median9.9
Q318.1
95-th percentile21.89
Maximum27.74
Range27
Interquartile range (IQR)12.97

Descriptive statistics

Standard deviation6.9981231
Coefficient of variation (CV)0.61966028
Kurtosis-1.2402092
Mean11.293483
Median Absolute Deviation (MAD)6.57
Skewness0.29043376
Sum3760.73
Variance48.973727
MonotonicityNot monotonic
2024-02-09T18:08:24.526701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 88
26.4%
19.58 21
 
6.3%
6.2 14
 
4.2%
8.14 13
 
3.9%
21.89 10
 
3.0%
9.9 9
 
2.7%
8.56 8
 
2.4%
3.97 8
 
2.4%
6.91 7
 
2.1%
4.05 7
 
2.1%
Other values (58) 148
44.4%
ValueCountFrequency (%)
0.74 1
0.3%
1.21 1
0.3%
1.22 1
0.3%
1.25 1
0.3%
1.32 1
0.3%
1.38 1
0.3%
1.47 1
0.3%
1.52 2
0.6%
1.69 1
0.3%
1.76 1
0.3%
ValueCountFrequency (%)
27.74 4
 
1.2%
25.65 6
 
1.8%
21.89 10
 
3.0%
19.58 21
 
6.3%
18.1 88
26.4%
15.04 2
 
0.6%
13.92 3
 
0.9%
13.89 1
 
0.3%
12.83 4
 
1.2%
11.93 4
 
1.2%

new_store
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size461.0 B
False
313 
True
 
20
ValueCountFrequency (%)
False 313
94.0%
True 20
 
6.0%
2024-02-09T18:08:24.632675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

commercial_property
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct77
Distinct (%)25.4%
Missing30
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean16.641254
Minimum1.75
Maximum1009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:24.734443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.75
5-th percentile3.06
Q15.45
median9.4
Q314.45
95-th percentile21
Maximum1009
Range1007.25
Interquartile range (IQR)9

Descriptive statistics

Standard deviation72.342194
Coefficient of variation (CV)4.34716
Kurtosis155.68149
Mean16.641254
Median Absolute Deviation (MAD)4.3
Skewness12.332348
Sum5042.3
Variance5233.3931
MonotonicityNot monotonic
2024-02-09T18:08:24.862563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 12
 
3.6%
18.15 12
 
3.6%
13.7 10
 
3.0%
4.35 10
 
3.0%
26.05 10
 
3.0%
12.75 10
 
3.0%
6.95 9
 
2.7%
9.7 8
 
2.4%
7.85 8
 
2.4%
19.5 8
 
2.4%
Other values (67) 206
61.9%
(Missing) 30
 
9.0%
ValueCountFrequency (%)
1.75 1
 
0.3%
1.95 1
 
0.3%
2.5 1
 
0.3%
2.55 3
0.9%
2.65 1
 
0.3%
2.7 1
 
0.3%
2.75 1
 
0.3%
2.95 2
0.6%
3 1
 
0.3%
3.05 4
1.2%
ValueCountFrequency (%)
1009 1
 
0.3%
767 1
 
0.3%
123 1
 
0.3%
26.05 10
3.0%
21 5
1.5%
19.5 8
2.4%
18.4 3
 
0.9%
18.15 12
3.6%
17.5 7
2.1%
17.15 6
1.8%

household_size
Real number (ℝ)

HIGH CORRELATION 

Distinct308
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2656186
Minimum0.561
Maximum5.725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:24.984063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.561
5-th percentile2.2376
Q12.884
median3.202
Q33.595
95-th percentile4.5014
Maximum5.725
Range5.164
Interquartile range (IQR)0.711

Descriptive statistics

Standard deviation0.70395158
Coefficient of variation (CV)0.21556454
Kurtosis2.0517526
Mean3.2656186
Median Absolute Deviation (MAD)0.333
Skewness0.28402752
Sum1087.451
Variance0.49554782
MonotonicityNot monotonic
2024-02-09T18:08:25.258635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.127 3
 
0.9%
3.229 3
 
0.9%
3.727 2
 
0.6%
2.875 2
 
0.6%
3.03 2
 
0.6%
3.144 2
 
0.6%
3.096 2
 
0.6%
3.122 2
 
0.6%
3.431 2
 
0.6%
2.304 2
 
0.6%
Other values (298) 311
93.4%
ValueCountFrequency (%)
0.561 1
0.3%
0.863 1
0.3%
1.138 1
0.3%
1.368 1
0.3%
1.519 1
0.3%
1.652 1
0.3%
1.906 1
0.3%
1.926 1
0.3%
1.963 1
0.3%
1.97 1
0.3%
ValueCountFrequency (%)
5.725 1
0.3%
5.398 1
0.3%
5.375 1
0.3%
5.337 1
0.3%
5.266 1
0.3%
5.259 1
0.3%
5.247 1
0.3%
5.04 1
0.3%
5.034 1
0.3%
4.929 1
0.3%

proportion_newbuilds
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct260
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.773574
Minimum0
Maximum94
Zeros26
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:25.379791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.2
median23.3
Q354.6
95-th percentile82.38
Maximum94
Range94
Interquartile range (IQR)48.4

Descriptive statistics

Standard deviation28.133344
Coefficient of variation (CV)0.88543215
Kurtosis-0.93608546
Mean31.773574
Median Absolute Deviation (MAD)20
Skewness0.60464361
Sum10580.6
Variance791.48502
MonotonicityNot monotonic
2024-02-09T18:08:25.511763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
 
7.8%
4 4
 
1.2%
23.5 3
 
0.9%
1.2 3
 
0.9%
78.6 3
 
0.9%
4.6 3
 
0.9%
2.7 3
 
0.9%
4.4 3
 
0.9%
58.9 2
 
0.6%
8.2 2
 
0.6%
Other values (250) 281
84.4%
ValueCountFrequency (%)
0 26
7.8%
0.7 1
 
0.3%
1.1 1
 
0.3%
1.2 3
 
0.9%
1.3 1
 
0.3%
1.5 1
 
0.3%
1.6 2
 
0.6%
1.8 2
 
0.6%
1.9 1
 
0.3%
2 1
 
0.3%
ValueCountFrequency (%)
94 1
0.3%
93.8 1
0.3%
93.5 1
0.3%
93.4 2
0.6%
92.2 2
0.6%
91.6 1
0.3%
91.1 1
0.3%
90.2 1
0.3%
90.1 1
0.3%
87 1
0.3%

public_transport_dist
Real number (ℝ)

HIGH CORRELATION 

Distinct295
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7099336
Minimum1.1296
Maximum10.7103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:25.638273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.5022
Q12.1224
median3.0923
Q35.1167
95-th percentile7.49938
Maximum10.7103
Range9.5807
Interquartile range (IQR)2.9943

Descriptive statistics

Standard deviation1.9811231
Coefficient of variation (CV)0.53400498
Kurtosis0.11355108
Mean3.7099336
Median Absolute Deviation (MAD)1.1622
Skewness0.93814296
Sum1235.4079
Variance3.9248485
MonotonicityNot monotonic
2024-02-09T18:08:25.769953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.4007 3
 
0.9%
4.8122 3
 
0.9%
5.7209 3
 
0.9%
5.4917 3
 
0.9%
5.2873 3
 
0.9%
6.8147 3
 
0.9%
3.6519 3
 
0.9%
6.4798 3
 
0.9%
3.2721 2
 
0.6%
6.4584 2
 
0.6%
Other values (285) 305
91.6%
ValueCountFrequency (%)
1.1296 1
0.3%
1.137 1
0.3%
1.1691 1
0.3%
1.1742 1
0.3%
1.2024 1
0.3%
1.2852 1
0.3%
1.3216 1
0.3%
1.3325 1
0.3%
1.3449 1
0.3%
1.358 1
0.3%
ValueCountFrequency (%)
10.7103 1
0.3%
9.2229 1
0.3%
9.1876 1
0.3%
9.0892 1
0.3%
8.9067 1
0.3%
8.7921 1
0.3%
8.6966 1
0.3%
8.5353 1
0.3%
8.344 1
0.3%
8.3248 1
0.3%
Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:25.879879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.8798799
Min length3

Characters and Unicode

Total characters1625
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowall
2nd rowaverage
3rd rowmany
4th rowno
5th rowaverage
ValueCountFrequency (%)
all 88
26.4%
average 76
22.8%
few 70
21.0%
no 55
16.5%
many 44
13.2%
2024-02-09T18:08:26.086222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
333
20.5%
a 284
17.5%
e 222
13.7%
l 176
10.8%
n 99
 
6.1%
v 76
 
4.7%
r 76
 
4.7%
g 76
 
4.7%
f 70
 
4.3%
w 70
 
4.3%
Other values (3) 143
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1292
79.5%
Space Separator 333
 
20.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 284
22.0%
e 222
17.2%
l 176
13.6%
n 99
 
7.7%
v 76
 
5.9%
r 76
 
5.9%
g 76
 
5.9%
f 70
 
5.4%
w 70
 
5.4%
o 55
 
4.3%
Other values (2) 88
 
6.8%
Space Separator
ValueCountFrequency (%)
333
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1292
79.5%
Common 333
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 284
22.0%
e 222
17.2%
l 176
13.6%
n 99
 
7.7%
v 76
 
5.9%
r 76
 
5.9%
g 76
 
5.9%
f 70
 
5.4%
w 70
 
5.4%
o 55
 
4.3%
Other values (2) 88
 
6.8%
Common
ValueCountFrequency (%)
333
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
333
20.5%
a 284
17.5%
e 222
13.7%
l 176
10.8%
n 99
 
6.1%
v 76
 
4.7%
r 76
 
4.7%
g 76
 
4.7%
f 70
 
4.3%
w 70
 
4.3%
Other values (3) 143
8.8%

property_value
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean409.27928
Minimum188
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:26.211811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum188
5-th percentile219.6
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range523
Interquartile range (IQR)387

Descriptive statistics

Standard deviation170.84199
Coefficient of variation (CV)0.41742154
Kurtosis-1.1907888
Mean409.27928
Median Absolute Deviation (MAD)78
Skewness0.63302676
Sum136290
Variance29186.985
MonotonicityNot monotonic
2024-02-09T18:08:26.345001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666 88
26.4%
307 27
 
8.1%
403 21
 
6.3%
437 10
 
3.0%
304 9
 
2.7%
398 9
 
2.7%
224 8
 
2.4%
296 8
 
2.4%
264 8
 
2.4%
384 8
 
2.4%
Other values (49) 137
41.1%
ValueCountFrequency (%)
188 6
1.8%
193 6
1.8%
198 1
 
0.3%
216 4
1.2%
222 5
1.5%
223 3
 
0.9%
224 8
2.4%
226 1
 
0.3%
233 7
2.1%
241 1
 
0.3%
ValueCountFrequency (%)
711 4
 
1.2%
666 88
26.4%
469 1
 
0.3%
437 10
 
3.0%
432 6
 
1.8%
430 2
 
0.6%
422 1
 
0.3%
411 1
 
0.3%
403 21
 
6.3%
402 1
 
0.3%

school_proximity
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)14.9%
Missing65
Missing (%)19.5%
Infinite0
Infinite (%)0.0%
Mean18.55597
Minimum13
Maximum21.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:26.473087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile14.7
Q117.4
median19.1
Q320.2
95-th percentile21
Maximum21.2
Range8.2
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1039649
Coefficient of variation (CV)0.1133848
Kurtosis-0.17055916
Mean18.55597
Median Absolute Deviation (MAD)1.1
Skewness-0.86026046
Sum4973
Variance4.4266683
MonotonicityNot monotonic
2024-02-09T18:08:26.583467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20.2 80
24.0%
14.7 15
 
4.5%
21 13
 
3.9%
18.4 11
 
3.3%
17.4 10
 
3.0%
19.1 10
 
3.0%
17.8 10
 
3.0%
16.6 9
 
2.7%
21.2 9
 
2.7%
18.6 9
 
2.7%
Other values (30) 92
27.6%
(Missing) 65
19.5%
ValueCountFrequency (%)
13 7
2.1%
13.6 1
 
0.3%
14.7 15
4.5%
14.9 1
 
0.3%
15.1 1
 
0.3%
15.2 7
2.1%
15.3 3
 
0.9%
15.6 2
 
0.6%
15.9 1
 
0.3%
16 3
 
0.9%
ValueCountFrequency (%)
21.2 9
 
2.7%
21.1 1
 
0.3%
21 13
 
3.9%
20.9 7
 
2.1%
20.2 80
24.0%
20.1 3
 
0.9%
19.7 4
 
1.2%
19.6 4
 
1.2%
19.2 7
 
2.1%
19.1 10
 
3.0%

competitor_density
Real number (ℝ)

Distinct237
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359.4661
Minimum3.5
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:26.706061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile97.462
Q1376.73
median392.05
Q3396.24
95-th percentile396.9
Maximum396.9
Range393.4
Interquartile range (IQR)19.51

Descriptive statistics

Standard deviation86.584567
Coefficient of variation (CV)0.24086991
Kurtosis8.0184665
Mean359.4661
Median Absolute Deviation (MAD)4.85
Skewness-2.9984217
Sum119702.21
Variance7496.8872
MonotonicityNot monotonic
2024-02-09T18:08:26.847021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9 79
 
23.7%
395.24 3
 
0.9%
395.63 2
 
0.6%
395.11 2
 
0.6%
393.68 2
 
0.6%
393.37 2
 
0.6%
388.45 2
 
0.6%
377.07 2
 
0.6%
396.21 2
 
0.6%
395.56 2
 
0.6%
Other values (227) 235
70.6%
ValueCountFrequency (%)
3.5 1
0.3%
3.65 1
0.3%
7.68 1
0.3%
9.32 1
0.3%
16.45 1
0.3%
18.82 1
0.3%
22.01 1
0.3%
27.25 1
0.3%
43.06 1
0.3%
48.45 1
0.3%
ValueCountFrequency (%)
396.9 79
23.7%
396.42 1
 
0.3%
396.33 1
 
0.3%
396.3 1
 
0.3%
396.28 1
 
0.3%
396.24 1
 
0.3%
396.21 2
 
0.6%
396.14 1
 
0.3%
396.06 1
 
0.3%
395.99 1
 
0.3%

household_affluency
Real number (ℝ)

HIGH CORRELATION 

Distinct310
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1288589
Minimum0.4325
Maximum9.4925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:26.981098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.4325
5-th percentile0.914
Q11.795
median2.7425
Q34.105
95-th percentile6.673
Maximum9.4925
Range9.06
Interquartile range (IQR)2.31

Descriptive statistics

Standard deviation1.7669452
Coefficient of variation (CV)0.56472512
Kurtosis0.74869625
Mean3.1288589
Median Absolute Deviation (MAD)1.1025
Skewness0.97832756
Sum1041.91
Variance3.1220953
MonotonicityNot monotonic
2024-02-09T18:08:27.108352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5325 3
 
0.9%
3.3175 2
 
0.6%
2.025 2
 
0.6%
1.9 2
 
0.6%
1.375 2
 
0.6%
1.59 2
 
0.6%
1.1125 2
 
0.6%
1.8475 2
 
0.6%
2.375 2
 
0.6%
2.6125 2
 
0.6%
Other values (300) 312
93.7%
ValueCountFrequency (%)
0.4325 1
0.3%
0.495 1
0.3%
0.6175 1
0.3%
0.7175 1
0.3%
0.72 1
0.3%
0.735 1
0.3%
0.74 1
0.3%
0.7525 1
0.3%
0.7825 1
0.3%
0.79 2
0.6%
ValueCountFrequency (%)
9.4925 1
0.3%
9.245 1
0.3%
8.6925 1
0.3%
8.6025 1
0.3%
7.9975 1
0.3%
7.6575 1
0.3%
7.6475 1
0.3%
7.42 1
0.3%
7.3875 1
0.3%
7.3825 1
0.3%

normalised_sales
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct188
Distinct (%)58.8%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean-0.016966731
Minimum-1.936974
Maximum2.9684773
Zeros0
Zeros (%)0.0%
Negative187
Negative (%)56.2%
Memory size2.7 KiB
2024-02-09T18:08:27.232357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.936974
5-th percentile-1.3374188
Q1-0.58524963
median-0.14375902
Q30.24322658
95-th percentile2.1852402
Maximum2.9684773
Range4.9054512
Interquartile range (IQR)0.82847621

Descriptive statistics

Standard deviation0.97856136
Coefficient of variation (CV)-57.675302
Kurtosis1.6704039
Mean-0.016966731
Median Absolute Deviation (MAD)0.4033371
Skewness1.1175046
Sum-5.4293541
Variance0.95758233
MonotonicityNot monotonic
2024-02-09T18:08:27.361612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.96847726 10
 
3.0%
-0.2364175427 5
 
1.5%
-0.3781305781 5
 
1.5%
-0.3672295754 5
 
1.5%
0.1233155474 5
 
1.5%
-0.5416456191 5
 
1.5%
0.03610752556 5
 
1.5%
0.2432265774 5
 
1.5%
0.003404517369 4
 
1.2%
0.177820561 4
 
1.2%
Other values (178) 267
80.2%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
-1.936973968 1
0.3%
-1.871567952 1
0.3%
-1.718953914 1
0.3%
-1.697151908 2
0.6%
-1.675349903 1
0.3%
-1.599042884 1
0.3%
-1.577240878 2
0.6%
-1.566339876 1
0.3%
-1.533636867 1
0.3%
-1.522735865 1
0.3%
ValueCountFrequency (%)
2.96847726 10
3.0%
2.804962219 1
 
0.3%
2.783160213 1
 
0.3%
2.53243715 1
 
0.3%
2.401625118 1
 
0.3%
2.259912082 1
 
0.3%
2.216308071 1
 
0.3%
2.183605063 1
 
0.3%
1.856574981 1
 
0.3%
1.649455929 1
 
0.3%

county
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.138138
Minimum0
Maximum144
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T18:08:27.485141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q145
median60
Q373
95-th percentile108.2
Maximum144
Range144
Interquartile range (IQR)28

Descriptive statistics

Standard deviation24.605541
Coefficient of variation (CV)0.40245814
Kurtosis1.0702259
Mean61.138138
Median Absolute Deviation (MAD)14
Skewness0.71908404
Sum20359
Variance605.43267
MonotonicityNot monotonic
2024-02-09T18:08:27.615004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 10
 
3.0%
50 10
 
3.0%
61 10
 
3.0%
72 9
 
2.7%
45 9
 
2.7%
63 9
 
2.7%
68 8
 
2.4%
62 8
 
2.4%
48 8
 
2.4%
69 8
 
2.4%
Other values (90) 244
73.3%
ValueCountFrequency (%)
0 1
 
0.3%
7 1
 
0.3%
9 1
 
0.3%
15 1
 
0.3%
16 1
 
0.3%
19 1
 
0.3%
20 1
 
0.3%
21 1
 
0.3%
22 2
0.6%
23 4
1.2%
ValueCountFrequency (%)
144 1
0.3%
140 1
0.3%
139 1
0.3%
138 1
0.3%
137 1
0.3%
133 1
0.3%
128 2
0.6%
123 1
0.3%
122 2
0.6%
116 2
0.6%

is_train
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size461.0 B
True
320 
False
 
13
ValueCountFrequency (%)
True 320
96.1%
False 13
 
3.9%
2024-02-09T18:08:27.718363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Interactions

2024-02-09T18:08:21.880384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:07.572942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.708866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.764676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.857152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.110973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.187011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.226028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.387677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.415362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.467749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:18.460781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.752229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.787739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.961677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:07.696422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.783203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.847838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.939838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.189857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.259000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.298882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.462431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.494018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.539975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:18.544276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.828287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.866887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:22.036027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:07.769655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.850882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.923643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:11.014693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.262607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.327501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.368331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.531497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.565620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.607627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:18.623320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.899147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.948459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:22.116186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:07.848641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.933352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.002694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:11.089917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.338248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.405734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.440058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.603852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.642096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.678012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:18.849812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.971898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.023568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:22.192546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:07.924822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.005677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.079878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:11.320802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.413838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.484223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.514619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.676956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.715191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.747902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:18.931520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.049165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.099105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:22.272938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.000699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.081941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.158354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:11.394357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.488788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.560561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.587090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.748123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.790699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.816859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.009516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.121755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.180059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:22.352667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.072912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.151681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.236404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:11.469217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.566757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.631659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.656749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.819266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.862402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.888403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.087396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.194122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.251044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:22.600461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.150157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.224338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.315820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:11.542061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.641662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.705575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.724238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.888449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.935102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.955077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.165525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.267237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.327204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:22.673413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.224756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.295612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.390197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:11.616130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.719431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.775563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.796983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.958619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.007711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:18.024656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.248571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.343389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.403774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:22.754413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.303049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.374927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.468144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:11.691617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.797000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.850114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.871760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.032028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.082807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:18.095885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.331305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.417411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.486075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:22.827550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.373222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.443854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.539108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:11.759864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.872225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.916990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.082124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.097945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.150519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:18.160617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.408221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.482848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.556899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:22.919618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.469745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.533027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.624658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:11.844356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.956630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.001186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.164198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.188894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.236592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:18.245265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.501813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.564548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.646754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:22.993286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.546410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.604748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.698609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:11.923415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.029662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.072296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.236338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.258491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.308679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:18.312162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.582373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.634506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.720352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:23.070430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:08.626208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:09.682187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:10.772658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:12.016856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:13.104304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:14.147519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:15.308039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:16.333474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:17.385254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:18.383177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:19.665091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:20.707579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:08:21.798143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-09T18:08:27.793590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
commercial_propertycompetitor_densitycountycrime_ratehousehold_affluencyhousehold_sizeis_trainlocation_idnew_storenormalised_salesproperty_valueproportion_flatsproportion_newbuildsproportion_nonretailpublic_transport_distschool_proximity
commercial_property1.000-0.291-0.3800.7860.604-0.3460.0000.0060.000-0.5510.641-0.622-0.7870.753-0.8570.399
competitor_density-0.2911.0000.075-0.344-0.1970.0550.0000.0520.0000.145-0.2950.1700.224-0.2950.245-0.076
county-0.3800.0751.000-0.382-0.6460.5000.086-0.0110.2040.709-0.4140.3230.387-0.4330.295-0.313
crime_rate0.786-0.344-0.3821.0000.628-0.3660.2440.0440.000-0.5450.734-0.562-0.6880.716-0.7160.461
household_affluency0.604-0.197-0.6460.6281.000-0.6460.000-0.0200.000-0.8620.536-0.472-0.6490.641-0.5680.447
household_size-0.3460.0550.500-0.366-0.6461.0000.130-0.0800.0000.641-0.3200.3990.304-0.4780.331-0.279
is_train0.0000.0000.0860.2440.0000.1301.0000.0210.000NaN-0.0150.0080.040-0.0020.0270.058
location_id0.0060.052-0.0110.044-0.020-0.0800.0211.0000.2010.033-0.0220.047-0.011-0.0000.0180.028
new_store0.0000.0000.2040.0000.0000.0000.0000.2011.0000.150-0.022-0.033-0.0530.058-0.063-0.095
normalised_sales-0.5510.1450.709-0.545-0.8620.641NaN0.0330.1501.000-0.5460.4420.562-0.5860.456-0.527
property_value0.641-0.295-0.4140.7340.536-0.320-0.015-0.022-0.022-0.5461.000-0.372-0.5390.663-0.5630.475
proportion_flats-0.6220.1700.323-0.562-0.4720.3990.0080.047-0.0330.442-0.3721.0000.541-0.6360.604-0.459
proportion_newbuilds-0.7870.2240.387-0.688-0.6490.3040.040-0.011-0.0530.562-0.5390.5411.000-0.6760.820-0.415
proportion_nonretail0.753-0.295-0.4330.7160.641-0.478-0.002-0.0000.058-0.5860.663-0.636-0.6761.000-0.7470.497
public_transport_dist-0.8570.2450.295-0.716-0.5680.3310.0270.018-0.0630.456-0.5630.6040.820-0.7471.000-0.372
school_proximity0.399-0.076-0.3130.4610.447-0.2790.0580.028-0.095-0.5270.475-0.459-0.4150.497-0.3721.000

Missing values

2024-02-09T18:08:23.198555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-09T18:08:23.420305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

location_idcrime_rateproportion_flatsproportion_nonretailnew_storecommercial_propertyhousehold_sizeproportion_newbuildspublic_transport_disttransport_availabilityproperty_valueschool_proximitycompetitor_densityhousehold_affluencynormalised_salescountyis_train
046417.6005410.018.10FalseNaN2.92629.02.9084all66620.2368.744.5325-0.39993340True
15040.60355620.03.97False14.854.52010.62.1398average26413.0388.371.81502.21630880True
22950.6068100.06.20False7.702.98131.93.6715many30717.4378.352.91250.16692053True
31870.01238555.02.25False1.953.45368.17.3073no30015.3394.722.0575-0.08380465True
41930.016182100.01.32False3.053.81659.58.3248average25615.1392.900.98750.96269397True
51600.0686590.011.93False11.153.9769.02.1675no27321.0396.901.41000.12331669True
6430.25412612.57.87False8.703.3775.76.3467average31115.2392.525.1125-0.84687422True
72786.5811310.018.10False9.103.24235.33.4242all66620.2396.902.68500.02520754True
838717.9221390.018.10False16.452.8964.61.9096all66620.27.686.0975-1.57724151True
9985.4377070.018.10False18.153.70110.02.5975all66620.2255.234.1050-0.69426047True
location_idcrime_rateproportion_flatsproportion_nonretailnew_storecommercial_propertyhousehold_sizeproportion_newbuildspublic_transport_disttransport_availabilityproperty_valueschool_proximitycompetitor_densityhousehold_affluencynormalised_salescountyis_train
3232271.5174090.019.58False12.753.0660.01.7573average40314.7353.891.6075NaN62False
32411483.0935330.018.10False16.452.9570.01.8026all66620.216.455.1550NaN22False
32520310.9883230.018.10False19.503.4062.82.0651all66620.2385.964.8800NaN19False
326120.1598950.06.91False4.903.16993.45.7209no23317.9383.371.4525NaN63False
3271999.3419920.018.10True15.902.87510.41.1296all66620.2347.882.2200NaN107False
3284770.01023890.02.97False2.504.08879.27.3073no28515.3394.721.9625NaN69False
3293410.2168020.07.38False7.153.43185.35.4159average28719.6393.681.2700NaN58False
3301365.7518920.018.10False18.153.2978.22.3682all666NaN385.094.3175NaN56False
3311480.6500780.06.20False7.855.33726.73.8384many30717.4385.910.6175NaN122False
3323630.1482220.08.56FalseNaN3.12714.82.1224average38420.9387.693.5225NaN63False